Growth differentiation factor-15 levels and the risk of contrast induced nephropathy in patients with acute myocardial infarction undergoing percutaneous coronary intervention: A retrospective observation study

Abstract

AimsTo investigate the association between growth differentiation factor-15 (GDF-15) and contrast-induced nephropathy (CIN) in patients with acute myocardial infarction (AMI) undergoing percutaneous coronary intervention (PCI).MethodsA total of 311 patients with AMI were studied retrospectively. All patients were divided into two groups according to the occurrence of CIN after PCI. Baseline clinical data were compared between two groups. Multivariate logistic regression analysis was used to identify the risk factors for CIN. Cox regression analysis was used to identify the association between GDF-15, CIN and short-term outcome.ResultsThere were 80 patients in CIN group (average age was 71.60 ± 13.00 years; 67.5% male) and 231 patients in non-CIN group (average age was 63.80 ± 11.70 years; 71.9%male). The concentration of GDF-15 in CIN group was higher than that of non-CIN group (1232 ± 366.6 ng/L vs. 939.20 ± 309.6 ng/L, P <0.001). According to GDF-15 quartiles, patients were divided into four groups. Multivariate logistic model indicated that the highest quartile(Q4) was significantly associated with an increased risk of CIN compared with lower level of GDF-15 (Q1, Q2 and Q3) (OR : 3.572, 1.803–7.078, P < 0.001). Of 243 patients who could calculate the ACEF risk score, area under the curve (AUC) of GDF-15 was 0.793, 95%CI: 0.729–0.856, P < 0.001, while AUC of ACEF was 0.708, 95%CI: 0.630–0.786, P < 0.001. Using 10% and 30% as arbitrary thresholds to define patients at low, intermediate, and high risk, GDF-15 achieved a net reclassification improvement (NRI) of 0.32 (95%CI: 0.123–0.518, P = 0.001) compared with the ACEF risk score. Cox regression model showed that high concentration of GDF-15 (Q4) was significantly associated with an increased risk of all-cause mortality and major adverse clinical events (MACE) (HR: 8.434, 95%CI: 2.650–26.837, P <0.001; HR: 3.562, 95%CI: 1.658–7.652, P = 0.001) compared with low level of GDF-15 (Q1, Q2 and Q3). CIN was an independent predictor of all-cause mortality and MACE in AMI patients (HR: 3.535, 95%CI: 1.135–11.005, P = 0.029; HR: 5.154, 95%CI: 2.228–11.925, P <0.001).ConclusionGDF-15 levels increased in CIN group in AMI patients underwent PCI. GDF-15 was an independent risk factor for CIN in AMI patients underwent PCI. GDF-15 level and CIN are independent risk factors for all-cause mortality and MACE in short-term follow-ups.

Development and validation of a novel combinatorial nomogram model to predict in-hospital deaths in heart failure patients

“…First, the NRI was calculated in the training cohort. Referring to reported studies [ 18 , 33 ], we used 10% and 30% as thresholds to define risk classes for low-risk (< 10%), intermediate risk (10%-30%), and high-risk (> 30%) patients, and compared with the LR1 model (< 10% for low risk, 10%-30% for intermediate risk, and 30% for highest risk), the LR2 model predicted an NRI of 8.92% for in-hospital all-cause mortality (Fig. 6 A).…”

Nomograms based on pre-operative parametric for prediction of short-term mortality in acute myocardial infarction patients treated invasively

“…First, net reclassification improvement (NRI) was calculated in the training cohort. Based on the previous studies [ 6 ], we used 10% and 30% as thresholds to define the risk grade of patients at low (<10%), intermediate (10 - 30%), and high risk (>30%), C-AMI model achieved an NRI of 23.5% as compared with the GRACE risk score (<10% as low risk, 10 - 30% as moderate risk, and 30% as highest risk) for predicting 30-day mortality ( Figure 5A ). In 49 patients with events within 30 days, 14 patients were correctly reclassified into a higher risk category by C-AMI model.…”

Machine Learning to Predict Contrast-Induced Acute Kidney Injury in Patients With Acute Myocardial Infarction

“…Thirdly, newer CI-AKI biomarkers, such as GDF-15 (12,13), cystatin C (22), and neutrophil gelatinase-associated lipocalin (NGAL) (23) were not included in the model because they are not generally detected at an early stage of the disease. Fourthly, serum creatinine was detected by the enzymatic method with creatininase coupled sarcosine oxidase.…”

“…Early identification of patients with AMI, who are likely to develop contrast induced acute kidney injury (CI-AKI) after an invasive treatment, will alert us to start an early therapy (e.g., iso-osmolar contrast media, fluids, pre-procedural statin) to preserve the renal function. Certain risk biomarkers (10,11) and predictive models (12,13) were reported to be capable of predicting the incidence of AKI. However, their predictive efficiency needs further improvement.…”